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Non-invasive Quality Measurement Techniques in Food
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Non-invasive Quality Measurement Techniques in Food

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 3810

Special Issue Editor

Dr. Hao Lin

E-Mail Website
Guest Editor
School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
Interests: non-destructive technologies; data analysis

Special Issue Information

Dear Colleagues,

The food industry continues to be faced with the need to supply high-quality and safe food products while addressing the issue of on-site quality monitoring of food processing. Non-invasive measurement refers to the method of inspecting and testing the structure, quality, status, and defects of the tested object without damaging or affecting the use performance of the tested object and the internal organization of the tested object, and it promises to meet the requirement of industrial needs. Usually, the process of non-invasive measurement is very fast, and the quality detection of the tested object can be completed in a short time, which can be applied to the rapid and high-throughput detection of the quality and safety of food materials in the modern food industry. Recent advances non-invasive measurement technologies have opened new opportunities for the food industry. However, many technical hurdles still lie ahead for the sensing techniques, detection modes, data analysis, modeling, food analytes, and application scenarios in the food industry. This Special Issue aims to disseminate novel research in the development and application of non-invasive measurement techniques and sensing techniques for the quality and safety inspection of agricultural and food products. Authors are welcome to submit original research and review papers covering but not limited to the following topics:

  • Instrumentation of innovative sensing techniques;
  • Quality monitoring of food and agricultural products during storage;
  • on-site quality monitoring of food processing;
  • Multi-sensors fusion in food inspection;
  • Image-procesing for food inspection;
  • Data processing and analysis methods;
  • Modeling for food inspection;
  • Artificial intelligence and robotics for food inspection.

Dr. Hao Lin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensing techniques
  • food processing
  • food storage
  • food quality and safety
  • on-site monitor
  • vis-NIR spectroscopy
  • raman spectroscopy
  • optical imaging
  • hyperspectral/multispectral imaging
  • electronic nose/tongue
  • digital image processing
  • chemometrics
  • data analysis
  • deep/machine learning

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Published Papers (2 papers)

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Research

15 pages, 3061 KiB  
Article
Nondestructive Estimation of Hazelnut (Corylus avellana L.) Terminal Velocity and Drag Coefficient Based on Some Fruit Physical Properties Using Machine Learning Algorithms
by Onder Kabas, Mehmet Kayakus and Georgiana Moiceanu
Foods 2023, 12(15), 2879; https://doi.org/10.3390/foods12152879 - 28 Jul 2023
Cited by 3 | Viewed by 1281
Abstract
Hazelnut culture originated in Turkey, which has the highest volume and area of hazelnut production in the world. For the design and sizing of equipment and structures in agricultural operations for the hazelnut industry, especially harvesting operations and post-harvest operations, it is essential [...] Read more.
Hazelnut culture originated in Turkey, which has the highest volume and area of hazelnut production in the world. For the design and sizing of equipment and structures in agricultural operations for the hazelnut industry, especially harvesting operations and post-harvest operations, it is essential that an understanding of hazelnuts’ aerodynamic properties, i.e., terminal velocity and drag coefficient, is acquired. In this study, the moisture, mass, density, projected area, surface area, and geometric diameter were used as independent variables in the data set, and the dependent variables terminal velocity and drag coefficient estimation were determined. In this study, logistic regression (LR), support vector regression (SVR), and artificial neural networks (ANNs) were used based on machine learning methods. When the results were evaluated according to R2 (determination coefficient), MSE (mean squared error), and MAE (mean absolute error) metrics, it was seen that the most successful models were the ANN, SVR, and LR, respectively. According to the R2 metric, the ANN method achieved 91.5% for the terminal velocity of hazelnuts and 85.9% for the drag coefficient of hazelnuts. Using the independent variables in the study, it was seen that the terminal velocity and drag coefficient value of hazelnuts could be successfully estimated. Full article
(This article belongs to the Special Issue Non-invasive Quality Measurement Techniques in Food)
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16 pages, 5908 KiB  
Article
Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
by Woranitta Sahachairungrueng, Anthony Keith Thompson, Anupun Terdwongworakul and Sontisuk Teerachaichayut
Foods 2023, 12(13), 2519; https://doi.org/10.3390/foods12132519 - 28 Jun 2023
Cited by 7 | Viewed by 2036
Abstract
Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective [...] Read more.
Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares–discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods. Full article
(This article belongs to the Special Issue Non-invasive Quality Measurement Techniques in Food)
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